在互联网流量中,大部分网络数据是正常用户的访问数据,只有很小的一部分是攻击数据。针对这一点,文中通过对SVM的深入研究,结合C-SVM模型与One-class SVM模型的优点,提出了一种高精度且拥有无监督特性的模型One Class Enhanced SVM(ONE-...在互联网流量中,大部分网络数据是正常用户的访问数据,只有很小的一部分是攻击数据。针对这一点,文中通过对SVM的深入研究,结合C-SVM模型与One-class SVM模型的优点,提出了一种高精度且拥有无监督特性的模型One Class Enhanced SVM(ONE-ESVM),该模型很适合入侵检测某类数据量比例很大而其他类型的数据量比例较小的场景。文中通过CSE-CIC-IDS2018数据集对该模型进行了验证,结果表明,ONE-ESVM除了拥有One-class SVM的无监督特性外,其预测正确率最高能达到95.81%,误报率最低至0.49%,其性能足以满足网络入侵检测系统的需求。展开更多
This paper proposed an universal steganalysis program based on quantification attack which can detect several kinds of data hiding algorithms for grayscale images. In practice, most techniques produce stego images tha...This paper proposed an universal steganalysis program based on quantification attack which can detect several kinds of data hiding algorithms for grayscale images. In practice, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Attacking the suspicious images using the quantization method, we can obtain statistically different from embedded-and-quantization attacked images and from quantization attacked-but-not-embedded sources. We have developed a technique based on one-class SVM for discriminating between cover-images and stego-images. Simulation results show our approach is able to distinguish between cover and stego images with reasonable accuracy.展开更多
MicroRNAs(miRNA) are small molecular non-coding RNAs that have important roles in the post-transcriptional mechanism of animal and plant. They are commonly 21-25 nucleotides (nt) long and derived from 60-90 nt RNA hai...MicroRNAs(miRNA) are small molecular non-coding RNAs that have important roles in the post-transcriptional mechanism of animal and plant. They are commonly 21-25 nucleotides (nt) long and derived from 60-90 nt RNA hairpin structures, called miRNA hairpins. A larger num-ber of sequence segments in the human genome have been computationally identified with such 60-90 nt hairpins, however a majority of them are not miRNA hairpins. Most computational meth-ods so far for predicting miRNA hairpins were based on a two-class classifier to distinguish between miRNA hairpins and other sequence segments with hairpin structures. The difficulty of these methods is how to select hairpins as negative examples of miRNA hairpins in the classifier-training datasets, since only a few miRNA hairpins are available. Therefore, their classifier may be mis-trained due to some false negative examples of the training dataset. In this paper, we introduce a one-class support vector machine (SVM) method to predict miRNA hair-pins among the hairpin structures. Different from existing methods for predicting miRNA hairpins, the one-class SVM model is trained only on the information of the miRNA class. We also illus-trate some examples of predicting miRNA hair-pins in human chromosomes 10, 15, and 21, where our method overcomes the above disad-vantages of existing two-class methods.展开更多
基金Science Fund of Shanghai Municipal Education Commission (03DZ13)
文摘This paper proposed an universal steganalysis program based on quantification attack which can detect several kinds of data hiding algorithms for grayscale images. In practice, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Attacking the suspicious images using the quantization method, we can obtain statistically different from embedded-and-quantization attacked images and from quantization attacked-but-not-embedded sources. We have developed a technique based on one-class SVM for discriminating between cover-images and stego-images. Simulation results show our approach is able to distinguish between cover and stego images with reasonable accuracy.
文摘MicroRNAs(miRNA) are small molecular non-coding RNAs that have important roles in the post-transcriptional mechanism of animal and plant. They are commonly 21-25 nucleotides (nt) long and derived from 60-90 nt RNA hairpin structures, called miRNA hairpins. A larger num-ber of sequence segments in the human genome have been computationally identified with such 60-90 nt hairpins, however a majority of them are not miRNA hairpins. Most computational meth-ods so far for predicting miRNA hairpins were based on a two-class classifier to distinguish between miRNA hairpins and other sequence segments with hairpin structures. The difficulty of these methods is how to select hairpins as negative examples of miRNA hairpins in the classifier-training datasets, since only a few miRNA hairpins are available. Therefore, their classifier may be mis-trained due to some false negative examples of the training dataset. In this paper, we introduce a one-class support vector machine (SVM) method to predict miRNA hair-pins among the hairpin structures. Different from existing methods for predicting miRNA hairpins, the one-class SVM model is trained only on the information of the miRNA class. We also illus-trate some examples of predicting miRNA hair-pins in human chromosomes 10, 15, and 21, where our method overcomes the above disad-vantages of existing two-class methods.
